Last updated: 2019-01-04

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  • ✖ R Markdown file: uncommitted changes The R Markdown is untracked by Git. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

  • ✖ Environment: objects present

    The global environment had objects present when the code in the R Markdown file was run. These objects can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment. Use wflow_publish or wflow_build to ensure that the code is always run in an empty environment.

    The following objects were defined in the global environment when these results were created:

    Name Class Size
    alignMerge cwlStepParam 301.6 Kb
    callVar cwlStepParam 204.5 Kb
    conv cwlStepParam 318.7 Kb
    files array 384 bytes
    fq1 list 832 bytes
    fq2 list 832 bytes
    inputList list 800 bytes
    mc3 cwlStepParam 90.6 Kb
    multiqc cwlParam 14.1 Kb
    paramList list 904 bytes
    rgs list 880 bytes
    rnaseq_Sf cwlStepParam 274.5 Kb
    samples list 592 bytes
    vcf data.frame 12.5 Kb

  • ✔ Seed: set.seed(20181116)

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Ignored files:
    Ignored:    analysis/back/

Untracked files:
    Untracked:  analysis/.nojekyll
    Untracked:  analysis/RNASeq.Rmd
    Untracked:  analysis/RNASeq.Rmd~
    Untracked:  analysis/Rcwl_RNASeq.Rmd
    Untracked:  analysis/Rcwl_RNASeq.Rmd~
    Untracked:  analysis/Rcwl_RNASeq.md
    Untracked:  data/HBR_Rep1_ERCC-Mix2_Build37-ErccTranscripts-chr22.read1.fastq.gz
    Untracked:  data/HBR_Rep1_ERCC-Mix2_Build37-ErccTranscripts-chr22.read2.fastq.gz
    Untracked:  data/HBR_Rep2_ERCC-Mix2_Build37-ErccTranscripts-chr22.read1.fastq.gz
    Untracked:  data/HBR_Rep2_ERCC-Mix2_Build37-ErccTranscripts-chr22.read2.fastq.gz
    Untracked:  data/HBR_Rep3_ERCC-Mix2_Build37-ErccTranscripts-chr22.read1.fastq.gz
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    Modified:   analysis/_site.yml
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Preface

The workflowr package provides a good way to manage a project and organize analysis scripts and documents. To start an example project, rnaseq, and new analysis RNASeq.Rmd with workflowr package.

## library(workflowr)
## wflow_start("rnaseq")
## wflow_open("analysis/RNASeq.Rmd")

Prepare test data

An RNASeq test data set can be downloaded from genomedata, which includes pair-end fastqs for 6 samples.

download.file("http://genomedata.org/rnaseq-tutorial/HBR_UHR_ERCC_ds_5pc.tar", "../data/HBR_UHR_ERCC_ds_5pc.tar)
untar("../data/HBR_UHR_ERCC_ds_5pc.tar", exdir = "../data/")
dir("data/")
 [1] "HBR_Rep1_ERCC-Mix2_Build37-ErccTranscripts-chr22.read1.fastq.gz"
 [2] "HBR_Rep1_ERCC-Mix2_Build37-ErccTranscripts-chr22.read2.fastq.gz"
 [3] "HBR_Rep2_ERCC-Mix2_Build37-ErccTranscripts-chr22.read1.fastq.gz"
 [4] "HBR_Rep2_ERCC-Mix2_Build37-ErccTranscripts-chr22.read2.fastq.gz"
 [5] "HBR_Rep3_ERCC-Mix2_Build37-ErccTranscripts-chr22.read1.fastq.gz"
 [6] "HBR_Rep3_ERCC-Mix2_Build37-ErccTranscripts-chr22.read2.fastq.gz"
 [7] "HBR_UHR_ERCC_ds_5pc.tar"                                        
 [8] "README.md"                                                      
 [9] "UHR_Rep1_ERCC-Mix1_Build37-ErccTranscripts-chr22.read1.fastq.gz"
[10] "UHR_Rep1_ERCC-Mix1_Build37-ErccTranscripts-chr22.read2.fastq.gz"
[11] "UHR_Rep2_ERCC-Mix1_Build37-ErccTranscripts-chr22.read1.fastq.gz"
[12] "UHR_Rep2_ERCC-Mix1_Build37-ErccTranscripts-chr22.read2.fastq.gz"
[13] "UHR_Rep3_ERCC-Mix1_Build37-ErccTranscripts-chr22.read1.fastq.gz"
[14] "UHR_Rep3_ERCC-Mix1_Build37-ErccTranscripts-chr22.read2.fastq.gz"

RNASeq Pipelines

The RNASeq pipeline was predefined in the RcwlPipeline package. All the dependent Bioinformatics tools have been deployed in docker repository (https://hub.docker.com/r/hubentu/rcwl-rnaseq/). We don’t need to install the tools one by one. Thus, the pipeline is totally portable.

First, we need to load the packages and pipeline.

if(!require(Rcwl)) BiocManager::install("hubentu/Rcwl")
if(!require(RcwlPipelines)) BiocManager::install("hubentu/RcwlPipelines")
suppressPackageStartupMessages(library(Rcwl))
suppressPackageStartupMessages(library(RcwlPipelines))
data(rnaseq_Sf)
short(rnaseq_Sf)
inputs:
- in_seqfiles
- in_prefix
- in_genomeDir
- in_GTFfile
- in_runThreadN
outputs:
- out_fastqc
- out_BAM
- out_Log
- out_Count
- out_idx
- out_stat
- out_count
- out_distribution
steps:
- fastqc
- STAR
- samtools_index
- samtools_flagstat
- featureCounts
- RSeQC

The function plotCWL can be used to visualize the relationship of inputs, outputs and the analysis for a pipeline:

plotCWL(rnaseq_Sf)

Only two steps to run the pipeline.

  1. To prepare input sample data and reference/annotation data.

  2. To submit the pipeline with SGE.

Prepare input list

The input data must be in a named list, with the same names as the input list of the pipeline.

inputs(rnaseq_Sf)
List of length 5
names(5): in_seqfiles in_prefix in_genomeDir in_GTFfile in_runThreadN

For this pipeline, 5 inputs are required to be set, including in_seqfiles, in_prefix, in_genomeDir, in_GTFfile and in_runThreadN.

There are two different input lists, inputList and paramList. The inputList is used to define the inputs for each sample and will be submitted to different cluster nodes. The paramList is used to define parameters which are shared in all jobs.

Two following inputs should be listed in inputList.

  • in_seqfiles: A list with the fastq files of each sample in each element. The names of the list are also required to be defined and can be the sample IDs. The length of the list will be the same as the number of samples, thus the list will be defined to inputList and assigned to different nodes for parallel computing.

  • in_prefix The same as in_seqfiles, which define a list of sample IDs.

files <- normalizePath(list.files("../data/", ".gz", full.names = TRUE))
files <- tapply(files, substring(basename(files), 1, 8), c)
inputList <- list(in_seqfiles = files,
                  in_prefix = as.list(names(files)))

These 3 parameter will be defined in paramList.

  • in_genomeDir: The reference genome indexes for STAR.

  • in_GTFfile: The gene annotation file in GTF format.

  • in_runThreadN: The number of threads to run for each job.

paramList <- list(
    in_genomeDir = "/rpcc/bioinformatics/reference/STAR/GRCh38_100/",
    in_sjdbGTFfile = "/rpcc/bioinformatics/annotation/GENECODE/gencode.v25.annotation.gtf",
    in_runThreadN = 4
)

Submit pipeline with SGE

The function runCWLBatch is used to submit the pipeline to cluster server. In addition to defining inputList and paramList, we need to define parallel parameters from the BiocParallel package. Here, we use “sge” to submit the jobs. The “sge” template is a bash script with some predefined parameters for “qsub”. The nodes queue name and number of slots/threads are variables from the template and can be assigned by the resources list.

res <- runCWLBatch(cwl = rnaseq_Sf, wdir = "../output/",
                   inputList = inputList, paramList = paramList,
                   BPPARAM = BatchtoolsParam(
                       workers = lengths(inputList)[1], cluster = "sge",
                       template = "/rpcc/bioinformatics/sge_centos7.tmpl",
                       resources = list(queue = "centos7.q",
                                        threads = 4)))

That’s it! The fastqc files of each sample will be submitted to different nodes to run the whole pipeline automatically.

All the results have been collected to output directory of each sample. For example,

dir("output/HBR_Rep1")
 [1] "HBR_Rep1_ERCC-Mix2_Build37-ErccTranscripts-chr22.read1_fastqc.zip"
 [2] "HBR_Rep1_ERCC-Mix2_Build37-ErccTranscripts-chr22.read2_fastqc.zip"
 [3] "HBR_Rep1.featureCounts.txt"                                       
 [4] "HBR_Rep1.featureCounts.txt.summary"                               
 [5] "HBR_Rep1Aligned.sortedByCoord.out.bam"                            
 [6] "HBR_Rep1Aligned.sortedByCoord.out.bam.bai"                        
 [7] "HBR_Rep1Aligned.sortedByCoord.out.distribution.txt"               
 [8] "HBR_Rep1Aligned.sortedByCoord.out.featureCounts.txt"              
 [9] "HBR_Rep1Aligned.sortedByCoord.out.featureCounts.txt.summary"      
[10] "HBR_Rep1Aligned.sortedByCoord.out.flagstat.txt"                   
[11] "HBR_Rep1Log.final.out"                                            
[12] "HBR_Rep1ReadsPerGene.out.tab"

Summarize QC

The tool “multiqc” can aggregate results from the multiple outputs of the pipeline and generate a single page report, which also was implemented in the RcwlPipelines package.

We can also run the tool using Rcwl locally with option noDocker = TRUE.

data(multiqc)
multiqc$dir <- "../output"
multiqc
class: cwlParam 
cwlClass: CommandLineTool 
cwlVersion: v1.0 
baseCommand: multiqc 
requirements:
- class: DockerRequirement
  dockerPull: hubentu/rcwl-rnaseq
inputs:
  dir (Directory):  /home/qhu/workspace/projects/RPipe/data/RNASeq/output
outputs:
  qc:
    type: File
      glob: *.html
runCWL(multiqc, stderr = "", Args = "--preserve-entire-environment", noDocker = TRUE)

Here we got the QC report:

multiqc_report.html

Build the reports

wflow_build()

Session information

sessionInfo()
R version 3.5.2 Patched (2018-12-31 r75935)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS release 6.4 (Final)

Matrix products: default
BLAS: /home/qhu/usr/R-3.5/lib64/R/lib/libRblas.so
LAPACK: /home/qhu/usr/R-3.5/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
[1] RcwlPipelines_0.0.0.9000 jsonlite_1.6            
[3] BiocParallel_1.16.2      Rcwl_0.99.7             
[5] S4Vectors_0.20.1         BiocGenerics_0.28.0     
[7] yaml_2.2.0               workflowr_1.1.1         

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0         tidyr_0.8.2        visNetwork_2.0.5  
 [4] prettyunits_1.0.2  assertthat_0.2.0   rprojroot_1.3-2   
 [7] digest_0.6.18      plyr_1.8.4         R6_2.3.0          
[10] backports_1.1.3    evaluate_0.12      highr_0.7         
[13] ggplot2_3.1.0      pillar_1.3.1       rlang_0.3.0.1     
[16] progress_1.2.0     lazyeval_0.2.1     rstudioapi_0.8    
[19] data.table_1.11.8  whisker_0.3-2      R.utils_2.7.0     
[22] R.oo_1.22.0        checkmate_1.8.5    rmarkdown_1.11    
[25] DiagrammeR_1.0.0   downloader_0.4     readr_1.3.1       
[28] stringr_1.3.1      htmlwidgets_1.3    igraph_1.2.2      
[31] munsell_0.5.0      compiler_3.5.2     influenceR_0.1.0  
[34] rgexf_0.15.3       xfun_0.4           pkgconfig_2.0.2   
[37] htmltools_0.3.6    tidyselect_0.2.5   gridExtra_2.3     
[40] tibble_1.4.2       batchtools_0.9.11  XML_3.98-1.16     
[43] viridisLite_0.3.0  crayon_1.3.4       dplyr_0.7.8       
[46] withr_2.1.2        R.methodsS3_1.7.1  rappdirs_0.3.1    
[49] grid_3.5.2         gtable_0.2.0       git2r_0.23.0      
[52] magrittr_1.5       scales_1.0.0       stringi_1.2.4     
[55] debugme_1.1.0      viridis_0.5.1      bindrcpp_0.2.2    
[58] brew_1.0-6         RColorBrewer_1.1-2 tools_3.5.2       
[61] glue_1.3.0         purrr_0.2.5        hms_0.4.2         
[64] Rook_1.1-1         colorspace_1.3-2   base64url_1.4     
[67] knitr_1.21         bindr_0.1.1

This reproducible R Markdown analysis was created with workflowr 1.1.1